Wind & Solar Track
18:45 - 20:15
Room: Douro IV + Douro VI
Submission 349
Characterization of Wind Power Ramps in the Brazilian Interconnected System
61 GIW26-349
Presented by: Mauricio B. C. Salles
Giovani Giulio Tristão Thibes Vieira 1, Walquiria N. Silva 2, Mauricio B. C. Salles 1, Renato M. Monaro 1
1 Escola Politécnica (EP-USP), Power Systems Innovation Hub (RCGI-InnovaPower), University of São Paulo, Brazil
2 Technological Institute of Aeronautics, ITA - Fortaleza (CE), Brazil

Brazil's National Interconnected System (SIN) has become one of the world's largest wind markets, with a fleet concentrated in the Northeast and South subsystems. As penetration deepens, fast variations in aggregate wind generation—wind ramp events—are emerging as a first-order operational constraint. Yet a country-wide, plant-level characterization of ramp frequency, severity and seasonality based on official operator data has not been published for Brazil. This paper introduces the first open ramp-event catalog for the SIN, built from 30-min plant-level generation and installed-capacity records published by the Brazilian System Operator (ONS) for January 2022 to June 2026. A streaming detection pipeline scans every plant time series and registers a ramp whenever the absolute change in active power exceeds 20% of the local installed capacity within an interval of up to three hours; overlapping events are deduplicated and plant names normalized. The resulting catalog contains 2,933,162 ramp events. Three findings stand out. First, ramps are common rather than exceptional: about one quarter of all 30-min intervals initiate a ramp, reaching 45% in Bahia in the late afternoon. Second, activity is sharply concentrated—Bahia and Rio Grande do Norte alone account for 63% of events—and frequency and severity decouple: the most ramp-prone states are not the most severe, and the diurnal signal appears in frequency but not in normalized severity. Third, absolute event counts and ramp rates (MW/h) grow with the fleet, whereas per-plant frequency and capacity-normalized severity remain statistically flat (Mann–Kendall p = 0.52), indicating that the growing ramp burden is fleet-driven rather than meteorological. These results suggest that ramp-aware planning anchored to national aggregates would systematically underserve the regions where operational stress is most acute.